Certolizumab pegol does not bind the neonatal Fc receptor (FcRn): Consequences for FcRn-mediated in vitro transcytosis and ex vivo human placental transfer
Bibliographic record
Abstract
Antibodies to tumor necrosis factor (anti-TNF) are used to treat inflammatory diseases, which often affect women of childbearing age. The active transfer of these antibodies across the placenta by binding of the Fc-region to the neonatal Fc receptor (FcRn) may result in adverse fetal or neonatal effects. In contrast to other anti-TNFs, certolizumab pegol lacks an Fc-region. The objective of this study was to determine whether the structure of certolizumab pegol limits active placental transfer. Binding affinities of certolizumab pegol, infliximab, adalimumab and etanercept to human FcRn and FcRn-mediated transcytosis were determined using in vitro assays. Human placentas were perfused ex vivo to measure transfer of certolizumab pegol and positive control anti-D IgG from the maternal to fetal circulation. FcRn binding affinity (KD) was 132nM, 225nM and 1500nM for infliximab, adalimumab and etanercept, respectively. There was no measurable certolizumab pegol binding affinity, similar to that of the negative control. FcRn-mediated transcytosis across a cell layer (mean±SD; n=3) was 249.6±25.0 (infliximab), 159.0±20.2 (adalimumab) and 81.3±13.1ng/mL (etanercept). Certolizumab pegol transcytosis (3.2±3.4ng/mL) was less than the negative control antibody (5.9±4.6ng/mL). No measurable transfer of certolizumab pegol from the maternal to the fetal circulation was observed in 5 out of 6 placentas that demonstrated positive-control IgG transport in the ex vivo perfusion model. Together these results support the hypothesis that the unique structure of certolizumab pegol limits its transfer through the placenta to the fetus and may be responsible for previously reported differences in transfer of other anti-TNFs from mother to fetus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".